In our model, a brain state represents the \(z\) -scored power across electrodes in eight logarithmically spaced frequency bands from 1 to 200 Hz. This choice was guided by (i) the goal of maintaining consistency with the brain states on which the memory classifier was trained, and (ii) the fact that power spectra are well-documented behavioral analogs for memory [1]}, [2]}, [3]}. Yet, since many power calculations require convolution with a sine wave, power is insensitive to non-sinusoidal and phase-dependent features of the signal [4]}, [5]}, [6]}. It would be interesting in future to explore transitions in other state spaces, such as instantaneous voltage [4]}. Lastly, it is important to note that our algorithm controls each frequency band independently, although incorporating inter-frequency coupling [8]}, [9]}, [10]} could be an interesting direction for future work.
